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This paper examines the time-varying efficiency of nine major Middle East and North Africa (MENA) stock markets namely Egypt, Bahrain, UAE, Jordan, Saudi Arabia, Oman, Qatar, Morocco and Tunisia during times of COVID-19 pandemic outbreak and vaccines.

The authors use two econometric approaches: (1) autocorrelation tests including the wild bootstrap automatic variance ratio test, the automatic portmanteau test and the Generalized spectral test, and (2) a non-Bayesian generalized least squares-based time-varying model with statistical inferences.

The results show that the degree of stock market efficiency of Egyptian, Bahraini, Saudi, Moroccan and Tunisian stock markets is influenced by the COVID-19 pandemic crisis. Furthermore, the authors find a tendency toward efficiency in most of the MENA markets after the announcement of the COVID-19's vaccine approval. Finally, the Jordanian, Omani, Qatari and UAE stock markets remain globally efficient during the three sub-periods of the COVID-19 pandemic outbreak.

The results have important implications for asset allocations and financial risk management. Portfolio managers may maximize the benefit of arbitrage opportunities by taking strategic long and short positions in these markets during downward trend periods. Policymakers should implement the action plans and reforms to protect the stock markets from global shocks and ensure the stability of the stock markets.

The data monetization market is valued at $1.5 billion, with an expected annual growth rate of 25%. This growth presents significant opportunities for companies to expand their revenue streams. However, many companies struggle to extract value from their data due to existing challenges and need for more knowledge. While existing studies describe and classify dimensions of the phenomenon, there is a need to explore causality relations that can help the structuring of data monetization processes. This study aimed to support the structuring of the data monetization process.

Proposing causality relations is important to explore the data monetization phenomenon. Therefore, empirical knowledge about data monetization was organized into design patterns using the context-intervention-mechanism-outcome (CIMO) logic. The effectiveness of these patterns was then assessed through an exploratory case study conducted at a leading Brazilian academic institution where data monetization is central to its business model.

The study yields six design patterns that address various aspects such as data pricing, data-driven business models and best practices for data monetization. Additionally, it presents a comprehensive understanding of the data monetization process through a value-added chain framework.

The findings contribute to the advancement of knowledge in the field, the proposition of causality, and offer valuable insights into organizations that wish to structure their resources and capabilities and leverage data.